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Data is the lifeblood of artificial intelligence (AI). Those who produce, own, or control access to data are critical stakeholders in the present and future of AI. Yet, data custodians face a paradox: They must protect their organisation’s sensitive data, but in doing so, they act as a blocker to realising the true value of that data in developing machine learning (ML) and AI models.

The Changing Landscape of AI

However, times are rapidly changing. As the first wave of AI hype begins to fade, organisations are awakening to the realisation that real value lies in leveraging their proprietary data for use by developers in building new, innovative models. But the big question remains: How to capitalise on the value of the data without compromising on privacy, governance, and security?

Challenges of the Past

Traditionally, sharing data was the only means to harness its power for AI — with the attendant risks of privacy and compliance breaches. Organisations faced the dilemma of either centralising data or providing direct access and relinquishing control, therefore opening themselves up to security breaches and diminishing the value of their data.

The New Way Forward

Today, however, there is a new way to leverage data without sharing it. By treating data as a product and governing what type of computations can be brought to it, data can be commercialised, and securely made available for use by others. Techniques such as federated learning and computational governance make this possible.

Data custodians can now retain control of proprietary data within a secure environment while making it available for machine learning applications. This not only enables growth and scalability for custodian organisations but also ensures compliance with the growing wave of AI and ML regulations, such as the EU AI Act’s stringent data privacy requirements.

A Paradigm Shift in Innovation

This paradigm shift is ushering in a new era of innovation. Companies, once grappling with small, bespoke models trained on limited datasets, are now capitalising on increasingly commoditized foundational models pre-trained on extensive publicly available datasets. This approach, with federated learning and computational governance, addresses the historical challenge of data scarcity, empowering companies to unlock the full potential of their proprietary datasets.

Applications Across Industries

By leveraging data for external AI use cases, enterprises secure a competitive edge in their markets. This not only contributes to individual business success but also propels AI towards tackling global challenges. Industries such as healthcare, financial services, retail, marketing, and manufacturing are witnessing the impact of securely making data available for AI use cases such as tackling fraud, optimising supply chains, reducing waste — and increasing productivity.

In the pharma and healthcare industry, for example, data custodians have an opportunity to unlock the value of sensitive data – contributing to enhanced drug discovery processes and more efficient clinical trials. Technologies like the Apheris Compute Gateway are facilitating collaboration among many of the top pharma companies and healthcare data providers, overcoming historical challenges in leveraging sensitive healthcare data.

However, industries dealing with sensitive data, such as healthcare, finance, or organisations in the public sector, face unique constraints. The extreme sensitivity of their data requires a nuanced approach – balancing the benefits of ML with the imperative to protect data integrity and privacy.

In conclusion, the evolving relationship between data privacy and AI is leading to innovative solutions that balance security with progress. The future of AI is not just about developing new models but also about how we responsibly and effectively utilise data while respecting privacy and governance.